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\fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 \sbasedon0 \snext69 toc 5;}{\s69\ql \li2560\ri0\sb60\sa60\keepn\nowidctlpar\tqr\tldot\tx8222\wrapdefault\faauto\rin0\lin2560\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 \sbasedon0 \snext69 toc 6;}}{\*\rsidtbl \rsid12853071\rsid13067347\rsid13964297}{\mmathPr\mmathFont0\mbrkBin0\mbrkBinSub0\msmallFrac0\mdispDef0\mlMargin0\mrMargin0\mwrapRight0\mintLim0\mnaryLim0}{\info{\title Original file was sonda.tex}{\doccomm Created using latex2rtf 2.3.3 r1230 (released Feb 26, 2013) on Thu Apr 25 11:30:15 2013}{\operator Samur Araujo}{\creatim\yr2013\mo4\dy25\hr11\min30}{\revtim\yr2013\mo4\dy25\hr12\min12}{\version3}{\edmins4}{\nofpages8}{\nofwords3944}{\nofchars22484}{\nofcharsws27611}{\vern33163}{\*\saveprevpict}}{\*\xmlnstbl {\xmlns1 http://schemas.microsoft.com/office/word/2003/wordml}}\paperw12280\paperh15900\margl2680\margr2700\margt2540\margb1760\gutter0\ltrsect \ftnbj\aenddoc\trackmoves0\trackformatting1\donotembedsysfont0\relyonvml0\donotembedlingdata1\grfdocevents0\validatexml0\showplaceholdtext0\ignoremixedcontent0\saveinvalidxml0\showxmlerrors0\aftnnar\horzdoc\dghspace120\dgvspace120\dghorigin1701\dgvorigin1984\dghshow0\dgvshow3\jcompress\viewkind1\viewscale150\rsidroot12853071 \fet0{\*\wgrffmtfilter 013f}\ilfomacatclnup0{\*\ftnsep \ltrpar \pard\plain \ltrpar\qj \li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \chftnsep \par }}{\*\ftnsepc \ltrpar \pard\plain \ltrpar\qj \li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \chftnsepc \par }}{\*\aftnsep \ltrpar \pard\plain \ltrpar\qj \li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \chftnsep \par }}{\*\aftnsepc \ltrpar \pard\plain \ltrpar\qj \li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \chftnsepc \par }}\ltrpar \sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\footerr \ltrpar \pard\plain \ltrpar\qc \li0\ri0\nowidctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \chpgn \par }}{\*\pnseclvl1\pnucrm\pnstart1\pnindent720\pnhang {\pntxta .}}{\*\pnseclvl2\pnucltr\pnstart1\pnindent720\pnhang {\pntxta .}}{\*\pnseclvl3\pndec\pnstart1\pnindent720\pnhang {\pntxta .}}{\*\pnseclvl4\pnlcltr\pnstart1\pnindent720\pnhang {\pntxta )}}{\*\pnseclvl5\pndec\pnstart1\pnindent720\pnhang {\pntxtb (}{\pntxta )}}{\*\pnseclvl6\pnlcltr\pnstart1\pnindent720\pnhang {\pntxtb (}{\pntxta )}}{\*\pnseclvl7\pnlcrm\pnstart1\pnindent720\pnhang {\pntxtb (}{\pntxta )}}{\*\pnseclvl8\pnlcltr\pnstart1\pnindent720\pnhang {\pntxtb (}{\pntxta )}}{\*\pnseclvl9\pnlcrm\pnstart1\pnindent720\pnhang {\pntxtb (}{\pntxta )}}\pard\plain \ltrpar\s3\ql \li0\ri0\sb120\sa120\keepn\widctlpar\wrapdefault\faauto\outlinelevel2\rin0\lin0\itap0 \rtlch\fcs1 \ab\af0\afs32 \ltrch\fcs0 \b\fs32\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 {\*\bkmkstart BMalg_candidateselection}4.3{\*\bkmkend BMalg_candidateselection}  Optimization Process for All Instances\par }\pard\plain \ltrpar\qj \li0\ri0\sb60\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 As discussed previously, computing the metrics to estimate optimality as well as learning to skip queries required information from \u8220\'d2some\u8221\'d3 previous runs. Here, we describe the iterative process in detail, where each iteration is concerned with processing one particular instance, and previous runs refer to the previous iterations that have been performed for different instances. \par }\pard \ltrpar\qc \li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 [Sorry. Ignored }{\rtlch\fcs1 \af2 \ltrch\fcs0 \f2\fs24\insrsid13067347 \\begin\{algorithm\} ... \\end\{algorithm\}}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]\par }\pard \ltrpar\qj \fi300\li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 The overall procedure is presented Alg. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMalg_candidateselection \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 2}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , which has three distinct phases applied to different subsets of source instances, namely }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Sorting}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Learning}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  and }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Predicting}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  phases. Fig. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMfig_branch \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 2}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  illustrates these three phases as well as the overall process. Clearly, these phases differ in the number of queries evaluated for each instance because in the beginning, the optimization is not effective as information necessary for estimation and learning have to be acquired first. \par }\pard\plain \ltrpar\s40\qc \fi300\li0\ri0\sb240\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \par }\pard\plain \ltrpar\s23\qc \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \par }\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 Figure {\*\bkmkstart BMfig_branch}2{\*\bkmkend BMfig_branch}: All queries are evaluated in the Sorting phase (black and dashed circles stand for optimal and \u8220\'d2unnecessary\u8221\'d3 queries, respectively), while less queries are evaluated in the Learning and Predicting phases (white circles denote unevaluated queries).}{\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \v\fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\tc {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 2 All queries are evaluated in the Sorting phase (black and dashed circles stand for optimal and unnecessary queries, respectively), while less queries are evaluated in the Learning and Predicting phases (white circles denote unevaluated queries).\tcf102}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \par }\pard\plain \ltrpar\qj \fi300\li0\ri0\sb240\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Sorting.}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  This phase covers \u946\'5f% of the instances (1% in the experiment, denoted by }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 B}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  in Alg. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMalg_sortingphase \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 3}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ). During this phase, all queries are evaluated to obtain the time and cardinality estimations. That is, for every }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 s}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  covered in this phase, queries are randomly selected and executed, and no skipping of queries is possible. At the end of this phase, the time-order of the queries is determined. \par }\pard \ltrpar\qc \li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 [Sorry. Ignored }{\rtlch\fcs1 \af2 \ltrch\fcs0 \f2\fs24\insrsid13067347 \\begin\{algorithm\} ... \\end\{algorithm\}}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]\par }\pard \ltrpar\qj \fi300\li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Learning.}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  During both the Learning (see Alg. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMalg_learningphase \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 4}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ) and Predicting phases, for every instance }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 s}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  and every }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 s}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 p}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 t}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ))}{\rtlch\fcs1 \af129 \ltrch\fcs0 \f129\insrsid13067347 \u8838\'5f}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 s}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , queries are executed according to the determined time-order until the termination condition is reached, i.e. until }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\up(}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs16\insrsid13067347 *,}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 s}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  with the lowest possible observed cardinality has been reached, i.e. observed }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 card}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 (}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )=1, or when all queries in }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 s}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  have been evaluated. Information collected previously as well as during this phase is exploited to learn the classifier for skipping queries as discussed in the previous section. The learning is performed until convergence, i.e. until the prediction becomes more stable as more and more data is exploited during the process (Alg. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMalg_sortingphase \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 3}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , line 9). In particular, we test the prediction quality to stop this process when prediction errors do not change for }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 n}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  instances (3 in the experiment). \par }\pard \ltrpar\qc \li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 [Sorry. Ignored }{\rtlch\fcs1 \af2 \ltrch\fcs0 \f2\fs24\insrsid13067347 \\begin\{algorithm\} ... \\end\{algorithm\}}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]\par }\pard \ltrpar\qj \fi300\li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 As an additional step, queries are updated with class components before the Learning phase (Alg. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMalg_candidateselection \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 2}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , lines 4-6). To obtain the examples }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ \u916\'5f\\s\\up5(}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs16\insrsid13067347 +}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  and }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ \u916\'5f\\s\\up5(}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs16\insrsid13067347 \u8722\'5f}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , the candidates obtained so far serve as input to an instance matcher that outputs the examples. Then, using }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ \u916\'5f\\s\\up5(}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs16\insrsid13067347 +}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  and }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ \u916\'5f\\s\\up5(}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs16\insrsid13067347 \u8722\'5f}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , the class components are learned, as discussed in Section 3.3, and the queries are updated accordingly. \par }\pard \ltrpar\qc \li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 [Sorry. Ignored }{\rtlch\fcs1 \af2 \ltrch\fcs0 \f2\fs24\insrsid13067347 \\begin\{algorithm\} ... \\end\{algorithm\}}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]\par }\pard \ltrpar\qj \li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Predicting.}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  Finally, the classifier learned in the previous phase is used to skip queries in this phase (see Alg. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMalg_predictingphase \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 5}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ), i.e. given }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 i}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs16\insrsid13067347 \u8722\'5f1}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  and }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 i}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , it executes }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 i}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  when the classifier yields true for the pair }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ (}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 i}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs16\insrsid13067347 \u8722\'5f1}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )\\,}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 i}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ))}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 . \par }\pard\plain \ltrpar\s2\ql \li0\ri0\sb240\sa120\keepn\widctlpar\wrapdefault\faauto\outlinelevel1\rin0\lin0\itap0 \rtlch\fcs1 \ab\af0\afs32 \ltrch\fcs0 \b\fs32\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 5  Evaluation\par }\pard\plain \ltrpar\qj \li0\ri0\sb60\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 There exist no solutions that can be directly applied to our on-the-fly integration problem. To compare Sonda, we designed two best-effort non-trivial baselines. They are based on S-based and S-agnostic, two recent candidate selection approaches we have adapted to the on-the-fly setting. Although we will refer to those baselines as S-based and S-agnostic, improvements reported in this paper do not refer to the original systems (which cannot be directly compared to Sonda) but the baselines.\par }\pard \ltrpar\qj \fi300\li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 Summarizing the experiments discussed in detail below, Sonda took 11 minutes (37m, with real Web endpoints) and achieved an average effectiveness of 85%, measured by F1. The best baseline, S-based, took 14m (82m, Web endpoints) and achieved 73% F1. To consider the effect of candidate selection on instance matching, we run SERIMI on top of Sonda\rquote s candidates and compared the results with those reported for the OAEI benchmark. As an average over all datasets, Sonda+SERIMI was the best system, and it resulted in 13% F1 improvement over SERIMI, indicating that Sonda effectively preserved the correct candidates and also reduced ambiguity (incorrect candidates), helping the matcher to achieve higher quality results. \par }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Datasets.}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  We relied on the datasets and ground truth published by OAEI\~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_journals_jods_EuzenatMSSS11 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 12}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]. We used the life science (LS) collection (which includes Sider, Drugbank, Dailymed TCM, and Diseasome) and the Person-Restaurant (PR) from the 2010 collection and all datasets from the 2011 collection. The matching tasks are cross-dataset tasks, which always involve a pair of datasets. One is the source while the other is treated as the target. \par }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Metrics.}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  For assessing candidate selection results, we employed standard metrics, namely Reduction Ratio (RR), Pair-wise Completeness (PC) and F1. Basically, high RR means that the candidate selection algorithm helps to focus on a smaller number of candidates, while high PC means that it preserves more of the correct candidates. More precisely, RR captures the reduction in the number of all possible candidate pairs that have to be considered for matching. A normalized version of RR can be used, when the number of all possible candidate pairs is large\~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_conf_semweb_SongH11 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 5}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]. We also use normalization, where instead of considering the reduction in the number of candidate pairs, we consider the reduction in the number of candidates. Beside these metrics, we also count the average number of queries evaluated per instance as well as the time needed. For assessing the instance matching results, we used the standard metrics Precision, Recall and F1. \par }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Systems.}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  Our system Sonda and the results of this experiments are available for download}{\rtlch\fcs1 \af0 \ltrch\fcs0 \cs58\super\insrsid13067347\charrsid13964297 \chftn {\footnote \ltrpar \pard\plain \ltrpar\s54\ql \fi-113\li397\ri0\widctlpar\wrapdefault\faauto\rin0\lin397\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \cs58\super\insrsid13067347\charrsid13964297 \chftn }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  https://github.com/samuraraujo/Sonda}}}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  at GitHub. It was implemented in Ruby and the queries were implemented as SPARQL queries issued over remote SPARQL endpoints. For the OAEI datasets, we could find a SPARQL endpoint}{\rtlch\fcs1 \af0 \ltrch\fcs0 \cs58\super\insrsid13067347\charrsid13964297 \chftn {\footnote \ltrpar \pard\plain \ltrpar\s54\ql \fi-113\li397\ri0\widctlpar\wrapdefault\faauto\rin0\lin397\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \cs58\super\insrsid13067347\charrsid13964297 \chftn }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  http://dbpedia.org/sparql}}}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  on the Web, which serves DBpedia (the largest out of all given datasets). This endpoint runs the OpenLink Virtuoso Universal Server version 06.04.3132 on Linux, using 4 server processes. For all other datasets, we employed the OpenLink Virtuoso Universal Server Version 6.1.5.3127 as a SPARQL endpoint, and run it on a server in our controlled environment with Intel Core 2 Duo, 2.4 GHz, 4 GB RAM, using a FUJITSU MHZ2250BH FFS G1 248 GB hard disk. We load these datasets into Virtuoso, creating the default S-P-O index and an inverted index as supported by Virtuoso that was used to support LIKE, AND and OR queries. \par We used two configurations. In the }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Web}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  configuration, the DBpedia endpoint on the Web is used while in the }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 controlled}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  one, all datasets are managed using the our controlled endpoint. The presented values are averages over five runs. For performance reasons, remote data endpoints stop processing according to a manually set query timeout. The one we used supports a query limit. A }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 query limit}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  of 100 for instance indicates that the endpoint should stop processing after 100 number of results have been retrieved. To evaluate the effect of }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 class components}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , we considered two versions of Sonda, namely without (Sonda-A) and with class components (Sonda-C). \par For comparison, we modified the }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 S-agnostic}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_papadakis_efficient_2011 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 13}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ] and }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 S-based}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_conf_semweb_SongH11 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 5}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ] approaches by translating their schemes to queries that are processed against endpoints. S-agnostic\rquote s scheme consists of all value tokens while S-based uses values of discriminative attributes. Accordingly, OR queries are created to consider all value tokens used by S-agnostic. These queries are executed sequentially and their results are aggregated to produce the candidate set. As discussed, our approach and S-based use discriminative and comparable attributes for candidate selection. To achieve this in the on-the-fly setting, we use the sampling procedure presented in Section 3.1 for both approaches. S-based applies an additional similarity function to further prune incorrect candidates retrieved from these queries. For comparison purposes, we apply this strategy to all approaches, using the same similarity function. \par In summary, S-agnostic uses only value tokens while S-based additionally, employs attributes (focusing on discriminative ones). Sonda-A extends S-based, considering 4 more query types and furthermore, implements the heuristic-based search optimization. Sonda-C extends Sonda-A with class components. \par }\pard\plain \ltrpar\s41\qc \li0\ri0\sb240\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  \par }\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 Table 1: Results of the three systems over all pairs of datasets, where Queries denotes the total number of queries issued by the system, Queries/Instance (Q/I) denotes the amount of queries evaluated per instance, and Learning(s) and Search(s) stands for the time needed for learning queries and executing them, respectively.}{\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs14 \ltrch\fcs0 \v\fs14\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\tc {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 1 Results of the three systems over all pairs of datasets, where Queries denotes the total number of queries issued by the system, Queries/Instance (Q/I) denotes the amount of queries evaluated per instance, and Learning(s) and Search(s) stands for the time needed for learning queries and executing them, respectively.\tcf116}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \par \par  \par }\pard\plain \ltrpar\s3\ql \li0\ri0\sb360\sa120\keepn\widctlpar\wrapdefault\faauto\outlinelevel2\rin0\lin0\itap0 \rtlch\fcs1 \ab\af0\afs32 \ltrch\fcs0 \b\fs32\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 5.1  Candidate Selection Results\par }\pard\plain \ltrpar\qj \li0\ri0\sb60\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 Table 1 shows an overview of the results. Compared to the baseline approaches over all 18 matching tasks, Sonda-A and Sonda-C could improve F1 score in 16 and 17 of the tasks, respectively. Average F1 values for Sonda-A, Sonda-C, S-based and S-agnostic are 80% and 85%, 73% and 64%, respectively. This translates to a 14% improvement that Sonda-C could achieve over the best baseline, S-based. Average time performance of Sonda-A, Sonda-C, S-based and S-agnostic are 10m, 11m, 14m and 16m, respectively. Thus, Sonda-A and Sonda-C were 34% and 22% faster than the fastest baseline, S-based, respectively. Since higher quality results often require more processing time, we also look at time performance results in the light of result quality. In particular, we look at the matching tasks for which the result quality was comparable among the systems (when differences in PC and F1 were <5%). For these tasks, Sonda-A and Sonda-C were more than 45% faster than the fastest baseline, S-agnostic.\par }\pard \ltrpar\qj \fi300\li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Task Complexity.}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  Differences in F1 values obtained for different tasks indicate their varying levels of complexity. Sonda-A and Sonda-C consistently outperformed the baselines over all tasks (with one exception, task 16, where results were comparable). Large improvements could be achieved for tasks 4-8, especially the two tasks that involved DBpedia. These tasks involve large datasets and thus, capture a larger amount of possible candidates that have to be considered for every instance. Sonda was more effective in dealing with this ambiguity. In particular, it was more effective both in finding the correct candidates and reducing the number of candidates as indicated by average PC and RR, respectively. Higher PC could be achieved because more query types were considered, thus incorporating a larger space of candidates. This however, does not come at the expense of RR. While S-based and S-agnostic use all their query results as candidates, Sonda selectively chooses the best queries and utilizes only their results as the candidate set. \par There are 4 problematic tasks where F1 values were <0.7 (tasks 5, 9, 11 and 13). Particularly difficult was task 9, which involves Geo Names. This dataset contains many instances with the same labels with only few additional information to disambiguate them. The strategies used by OAEI matching systems to deal with this task is to manually encode and exploit geo- and location-specific knowledge in the form of rules (which were not used by our systems). Task 11 involves an artificial dataset where syntax mistakes where added to produce string level ambiguity. Sonda\rquote s PC values (58.47 and 59.32, respectively) were lower than those achieved by the baselines. Here we can clearly see the strategy of aggregating all queries results works well, while the heuristics used by Sonda\rquote s to choose only the best ones may compromise PC. However, it has a positive effect on RR, resulting in higher F1 also for this task. \par }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Attribute Components.}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  On average, systems using attribute components, Sonda-C, Sonda-A and S-based, are more effective than S-agnostic, which dismisses attribute information and used value tokens only. In terms of F1, their values are 85%, 81% and 73%, respectively, compared to 64%. \par }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Class Components.}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  The effect of the class component can be seen in differences between Sonda-C and Sonda-A. The former achieved a higher RR higher and comparable PC, thus indicating the class component has the positive effect of reducing the number of incorrect candidates. Especially for tasks 4 and 5 that involve DBpedia, improvements in RR were large (from 47.6 to 87.63 and 22.76 to 62.52, respectively). The class component has a stronger effect here because this dataset simply captures more candidate results, thus there is potentially also a higher number of incorrect results that could be pruned. \par }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Processing Cost}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 . As captured by Table 1, overall processing cost can be decomposed into learning and execution times. While learning is essential to produce the queries capturing different candidates, execution is needed to retrieve them. Thus, both steps are crucial for result quality. However, while the baselines execute all the learned queries, Sonda does not. This has a large impact on performance as we can see that learning is relatively cheap, which makes up only 7% of total time, on average. Due to the sampling we performed, the number of queries needed to retrieve data during learning is substantially smaller. Although Sonda-A and Sonda-C were 4.7x and 3.8x slower than S-agnostic during learning, the fastest approach that simply maps value tokens to queries, they were 41% and 36% faster than S-agnostic when considering the whole process. Thus, the results shows that although Sonda invested more time to learn the queries (which are needed to achieve the better results), the optimization could reduce the time in executing the queries. While we focus the discussion on times achieved for the controlled configuration because values were more stable, Table 1 also shows the differences between the controlled and Web configurations. Compared to its controlled version, Sonda-A, Sonda-C, S-based and S-agnostic were 3.6x, 3.3x, 5.8x and 1.9x slower, respectively. This suggests that delays caused by the external DBpedia endpoint have the largest negative impact on S-based. Accordingly, the performance improvement Sonda could achieve over S-based is larger in the Web setting. S-agnostic yields best time performance here because as opposed to the other systems, it does not require learning and thus, does not have retrieve data samples from the Web endpoints. \par }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Number of Queries}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 . This connection between cost and quality can be more clearly seen in the number of queries and F1. Sonda-C, Sonda-A, S-based and S-agnostic considered (during learning) on average 31, 20, 4 and 5 queries, which translates to F1 values of 85%, 81%, 73% and 64%, respectively. In all cases, Sonda achieves a considerable reduction in the number of queries evaluated per instance (during execution). In some cases (e.g. tasks 10, 14 and 18), it performed close to one query per instance.\par }\pard\plain \ltrpar\s40\qc \fi300\li0\ri0\sb240\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347   \par }\pard\plain \ltrpar\s23\qc \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  \par }\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 Figure {\*\bkmkstart BMfig_limitsagnostic}3{\*\bkmkend BMfig_limitsagnostic}: F1 for Sonda-A, S-agnostic and S-based for query limits 10, 30, 50 and 100.}{\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \v\fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\tc {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 3 F1 for Sonda-A, S-agnostic and S-based for query limits 10, 30, 50 and 100.\tcf102}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \par }\pard\plain \ltrpar\s40\qc \fi300\li0\ri0\sb380\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347   \par }\pard\plain \ltrpar\s23\qc \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  \par }\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 Figure {\*\bkmkstart BMfig_limitsbased}4{\*\bkmkend BMfig_limitsbased}: Execution time for Sonda-A, S-agnostic and S-based for query limits 10, 30, 50 and 100.}{\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \v\fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\tc {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 4 Execution time for Sonda-A, S-agnostic and S-based for query limits 10, 30, 50 and 100.\tcf102}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \par }\pard\plain \ltrpar\qj \fi300\li0\ri0\sb220\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Number of Results}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 . Also, quality (F1) is related to the number of results retrieved by the queries. Fig. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMfig_limitsagnostic \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 3}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  and Fig. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMfig_limitsbased \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 4}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  show the effect of query limit on Sonda-A, S-agnostic and S-based. Four query limits were used: 10, 30, 50, 100 elements. We can see that only F1 values for S-based improved consistently with increasing query limit, while execution times for both S-based and S-agnostic were higher with increasing query limit. This effect on time however, could not be observed for Sonda-A because due to the optimization, a small value for query limit sometimes resulted in a greater number of queries that have to be executed. We observed that while PC consistently improved with increasing limit (more results are incorporated), RR sometimes got worse with increase limit (because more results also include more negative matches). Thus, increasing query limit has a mixed impact on F1, while for the baselines, it unambiguously resulted in higher processing cost. \par }\pard \ltrpar\qj \fi300\li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\insrsid13067347 Query Types}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 . Fig. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMfig_frequency \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 5}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  shows for each task the percentage of query types executed to find the optimal candidate set. It illustrates that to produce non-empty candidate sets, all query types were considered useful by Sonda-A. \par }\pard\plain \ltrpar\s40\qc \fi300\li0\ri0\sb240\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347   \par }\pard\plain \ltrpar\s23\qc \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  \par }\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 Figure {\*\bkmkstart BMfig_frequency}5{\*\bkmkend BMfig_frequency}: Percentages of query types executed by Sonda-A per task.}{\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \v\fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\tc {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 5 Percentages of query types executed by Sonda-A per task.\tcf102}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \par }\pard\plain \ltrpar\s3\ql \li0\ri0\sb160\sa120\keepn\widctlpar\wrapdefault\faauto\outlinelevel2\rin0\lin0\itap0 \rtlch\fcs1 \ab\af0\afs32 \ltrch\fcs0 \b\fs32\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 5.2  Instance Matching Results\par }\pard\plain \ltrpar\qj \li0\ri0\sb60\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 To produce final instance matches for the OAEI matching tasks, we refined the candidates produced by Sonda using SERIMI (Sonda+SERIMI). Table 2 shows the OAEI 2010 results for Sonda+SERIMI, SERIMI (without Sonda) and the other systems, RIMON and ObjectCoref. When considering only datasets supported by ObjectCoref, we can see that ObjectCoref was second best (see Average-ObjectCoref), while over the datasets used by Rimon, SERIMI was second best (see Average-Rimon). Sonda+SERIMI was best over all three combinations of datasets. As an average over all datasets, Sonda+SERIMI resulted in 13% average improvement over SERIMI. Thus, these results suggest that Sonda was effective in preserving correct matches and reducing ambiguities (incorrect matches). This facilitates the instance matching task, enabling SERIMI to produce higher quality results. \par }\pard\plain \ltrpar\s41\qc \li0\ri0\sb240\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \par }\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 Table 2: Sonda+SERIMI compared to other OAEI 2010 published results.}{\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs14 \ltrch\fcs0 \v\fs14\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\tc {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 2 Sonda+SERIMI compared to other OAEI 2010 published results.\tcf116}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \par \par \ltrrow}\trowd \irow0\irowband0\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrs\brdrw15 \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrs\brdrw15 \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrs\brdrw15 \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrs\brdrw15 \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrs\brdrw15 \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\pard\plain \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Task\cell Sonda+SERIMI\cell SERIMI\cell ObjectCoref\cell Rimon\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow0\irowband0\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrs\brdrw15 \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrs\brdrw15 \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrs\brdrw15 \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrs\brdrw15 \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrs\brdrw15 \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\trowd \irow1\irowband1\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Sider-Dailymed\cell 0.63\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 0.66}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \cell -\cell 0.62\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow1\irowband1\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Sider-Diseasome\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 0.91}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \cell 0.87\cell -\cell 0.45\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow2\irowband2\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Sider-Drugbank\cell 0.95\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 0.97}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \cell -\cell 0.50\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow3\irowband3\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Sider-TCM\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 0.99}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \cell 0.97\cell -\cell 0.79\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow4\irowband4\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Dailymed-Sider\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 1.0}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \cell 0.67\cell 0.70\cell 0.62\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow5\irowband5\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Drugbank-Sider\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 1.0 }{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \cell 0.48\cell 0.46\cell -\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow6\irowband6\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Diseasome-Sider\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 0.97}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \cell 0.87\cell 0.74\cell -\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow7\irowband7\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Person11-Person12\cell 0.97\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 1.00}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \cell 0.99\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 1.00}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow8\irowband8\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Person21-Person22\cell 0.43\cell 0.46\cell 0.95\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 0.97}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow9\irowband9\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Restaurant1-Rest.2\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347  0.98}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \cell 0.77\cell 0.81\cell 0.88\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow10\irowband10\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Average-All\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 0.88}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  (+13%)\cell 0.77\cell -\cell -\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow11\irowband11\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Average-ObjectCoref\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 0.89}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  (+12%)\cell 0.71\cell 0.78\cell -\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow12\irowband12\ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row \ltrrow}\pard \ltrpar\qc \li0\ri0\widctlpar\intbl\wrapdefault\faauto\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  Average-Rimon\cell }{\rtlch\fcs1 \ab\af0 \ltrch\fcs0 \b\fs14\insrsid13067347 0.86}{\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  (+8%)\cell 0.80\cell -\cell 0.73\cell }\pard \ltrpar\ql \li0\ri0\widctlpar\intbl\wrapdefault\aspalpha\aspnum\faauto\adjustright\rin0\lin0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347 \trowd \irow13\irowband13\lastrow \ltrrow\ts11\trleft0\trftsWidth1\tblind8\tblindtype3 \clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth2508\clshdrawnil \cellx2508\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth1545\clshdrawnil \cellx4053\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx5047\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth856\clshdrawnil \cellx5903\clvertalt\clbrdrt\brdrnone \clbrdrl\brdrs\brdrw15 \clbrdrb\brdrs\brdrw15 \clbrdrr\brdrs\brdrw15 \cltxlrtb\clftsWidth3\clwWidth994\clshdrawnil \cellx6897\row }\pard\plain \ltrpar\s39\ql \li0\ri0\sb120\sa120\keep\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \fs14\insrsid13067347  \par }\pard\plain \ltrpar\s3\ql \li0\ri0\sb360\sa120\keepn\widctlpar\wrapdefault\faauto\outlinelevel2\rin0\lin0\itap0 \rtlch\fcs1 \ab\af0\afs32 \ltrch\fcs0 \b\fs32\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 5.3  Utility of the Approach\par }\pard\plain \ltrpar\qj \li0\ri0\sb60\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 Finally, in this section, we investigate when it is more efficient to query all instances from the remote endpoint and to perform candidate selection locally instead of executing instance-specific queries against the remote endpoint. This is the case when: \par }\pard\plain \ltrpar\s25\ql \li0\ri0\sb120\sa120\keep\widctlpar\tqc\tx3450\tqr\tx6900\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \tab }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \tab ({\*\bkmkstart BMeq_tradeoff_condition}1{\*\bkmkend BMeq_tradeoff_condition})\par }\pard\plain \ltrpar\qj \fi300\li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 where }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 s}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  is a set of instance-specific queries for source instance }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 s}{\rtlch\fcs1 \af3 \ltrch\fcs0 \f3\insrsid13067347 \u8712\'08}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 S}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  and }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 T}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  is a set of queries that retrieve all instances in the target dataset. If we consider }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 t}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 S}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  and }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 t}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 T}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  as estimated average time of the queries in }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 s}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  and }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 T}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , respectively; then Eq. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMeq_tradeoff_condition \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 1}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  can be approximated by:\par }\pard\plain \ltrpar\s25\ql \li0\ri0\sb120\sa120\keep\widctlpar\tqc\tx3450\tqr\tx6900\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs24 \ltrch\fcs0 \fs24\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \tab }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \tab ({\*\bkmkstart BMeq_tradeoff_condition2}2{\*\bkmkend BMeq_tradeoff_condition2})\par }\pard\plain \ltrpar\qj \fi300\li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 The most straightforward query that retrieves the entire content of an endpoint is the SPARQL query }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 "select * where \{? s ? p ? o\}"}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 . In this case, }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ |}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 T}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )|=1}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , and consequently, the inequality in Eq. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMeq_tradeoff_condition2 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 2}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  would depend entirely on the time }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 t}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 T}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 . For relatively small dataset with a few thousand triples, this query can be reasonably fast. For large datasets, it timeouts, because in practice endpoints impose a limit on the time spent on processing a query (or number of triples that can be retrieved by a query). Instead, the query}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347  "select * where \{? s ? p ? o\} limit X offset Y" }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 can be considered, where }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 X}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  represents the number of instances retrieved and }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Y}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 =}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 X}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \u215\'5f}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 i}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  where }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 i}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 =[0\\, \\F(|}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 T}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 |,}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 X}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )]}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 . In this case, }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ |}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 T}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )|= \\F(|}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 T}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 |,}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 X}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 .\par To obtain some numbers for Eq. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMeq_tradeoff_condition2 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 2}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  using real-world data, we selected the NYTimes dataset from the OAEI benchmark, as an example. It contains 350.000 triples in total, which was loaded using the same system configuration discussed before. We considered one attribute component and 5 query types; consequently,  }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ |}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 s}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )|=5}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 . We obtained }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 t}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 S}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )=0.02}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 s}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  for these 5 query types. Assuming a limit of 1000 (i.e., }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ |}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Q}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 T}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )|= \\F(350.000,1000)=350}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ), we obtained }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  EQ }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 t}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \\s\\do5(}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\fs16\insrsid13067347 T}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 )=2.65}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 s}}{\fldrslt }}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 . With these values, Eq. }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BMeq_tradeoff_condition2 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 2}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  can be reduced to |}{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 S}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 |<9.275, i.e., for this example, our method is more efficient than the alternative method of retrieving all instances, when }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 S}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  contains less than 9.275 instances. \par We note this is the case for all source datasets in the OAEI benchmark, i.e. they have no more than 5000 instances. Moreover, most of the target datasets in this benchmark contains millions of triples. It is clear that it is not efficient to download millions of triples when only a few thousands of them might be relevant for the integration task. These results suggest that in real-world datasets and integration tasks, downloading all data is not always needed. Our on-the-fly matching solution that selects only necessary candidates helps to improve performance, especially when the number of instances to be matched is small. \par }\pard\plain \ltrpar\s2\ql \li0\ri0\sb240\sa120\keepn\widctlpar\wrapdefault\faauto\outlinelevel1\rin0\lin0\itap0 \rtlch\fcs1 \ab\af0\afs32 \ltrch\fcs0 \b\fs32\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 6  Related Work\par }\pard\plain \ltrpar\qj \li0\ri0\sb60\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 Candidate selection}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  (blocking) techniques [}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_hernandez_merge_purge_1995 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 2}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ] aim at more efficient instance matching by reducing the number of similarity comparisons between instances. As blocking keys, the set of all tokens that can be extracted from the instance data has been used\~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_conf_wsdm_PapadakisINF11 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 9}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]. This approach may yield too many incorrect candidates (as shown in our experiment). The authors tackled this problem by extracting several such profiles and combining them\~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_conf_wsdm_PapadakisINPN12 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 14}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]. In principle, this is similar to using several attributes as keys. There are works, which have shown that the most discriminative keys can be selected by considering their discriminative power and coverage\~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_conf_semweb_SongH11 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 5}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]. \par }\pard \ltrpar\qj \fi300\li0\ri0\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 For }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 matching}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , various learning-based approaches exist that can be further distinguished in terms of training data and degree of supervision, respectively (i.e. supervised, semi-supervised, unsupervised [}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_conf_semweb_SongH11 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 5}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_hu_bootstrapping_2011 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 15}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_journals_tkde_LiTLL09 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 16}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 , }{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_conf_vldb_ChaudhuriCGK07 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 8}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]). These approaches focus on learning instance matching schemes that are optimized solely towards result quality. There are also works that address the efficiency of learning. In particular, the efficiency of learning has been also studied at query time, where the goal is to minimize the amount of data needed for learning\~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_journals_jair_BhattacharyaG07 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 17}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]. The authors perform collective matching, which improves the quality of matches by considering the similarities of nodes related to these matches (the collective). Techniques have been proposed to reduce the number of related nodes that have to be considered\~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_journals_jair_BhattacharyaG07 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 17}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]. In this work, we show how the number of queries can be minimized to efficiently compute the matches. The proposed technique for retrieving related nodes\~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_journals_jair_BhattacharyaG07 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 17}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ] can be applied in addition (for collective matching). Furthermore, we also optimize the queries towards execution efficiency. \par In }{\rtlch\fcs1 \ai\af0 \ltrch\fcs0 \i\insrsid13067347 On-the-fly integration}{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  scenario, prior research on similarity joins and efficient indexes can be exploited for faster execution\~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_journals_pvldb_MetwallyF12 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 18}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]. They are complementary to our work. We aim to select efficient queries while these works can be exploited (and implemented by the endpoints) to further optimize the processing of these queries. In principle, SERIMI\~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_conf_webdb_AraujoTDHS12 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 7}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ] is able to perform instance matching over SPARQL endpoints. It focuses on refining results using a class-based strategy for pruning those, that do not belong to the class of interest. We take this idea to design class components. However, while SERIMI uses a set of instances as an instance-based class representation, we learn the class component (an explicit query-based representation of class) from data. Further, SERIMI focuses on the quality results but does not pay attention to the cost of obtaining these results, i.e. execution efficiency. Also focusing on result quality, it has been shown that through on-the-fly instance matching, entity search results can be improved\~[}{\field{\*\fldinst {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 REF BIB_DBLP_conf_www_HerzigT12 \\* MERGEFORMAT }}{\fldrslt {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 19}}}\sectd \ltrsect\linex0\cols2\colsx709\sectdefaultcl\sftnbj {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 ]. As opposed to these works, we propose to optimize for both result quality and execution efficiency. \par }\pard\plain \ltrpar\s2\ql \li0\ri0\sb240\sa120\keepn\widctlpar\wrapdefault\faauto\outlinelevel1\rin0\lin0\itap0 \rtlch\fcs1 \ab\af0\afs32 \ltrch\fcs0 \b\fs32\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 7  Conclusions\par }\pard\plain \ltrpar\qj \li0\ri0\sb60\widctlpar\wrapdefault\faauto\rin0\lin0\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 We proposed a candidate selection approach that operates by querying remote data endpoints in the Linked Data. Our method focuses on optimizing the quality of the results as well as optimizing the execution time to obtain them. To achieve high quality results, we learn from the data, candidate selection schemes that are used to build effective instance-specific queries. To achieve time performance, we employ a heuristic-based search algorithm that learns to efficiently execute those queries. We evaluate our approach over two baseline, using two benchmark matching task, OAEI 2010 and 2011. The results indicate that the use of schema information in the queries improves considerably the quality of the results and the overall execution time (because limit the scope where the queries are computed). Overall, compared to the best baseline, Sonda was 34% faster and improved the quality by 13%, measured in terms of F1. \par }\pard\plain \ltrpar\s52\ql \li450\ri0\widctlpar\wrapdefault\faauto\rin0\lin450\itap0 \rtlch\fcs1 \af0\afs20 \ltrch\fcs0 \fs20\lang1024\langfe1024\cgrid\noproof\langnp1033\langfenp1033 {\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347  }{\rtlch\fcs1 \af0 \ltrch\fcs0 \insrsid13067347 \par }{\*\themedata 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